Returns
List of Documents most similar to the query and score for each
class langchain.vectorstores.Qdrant(client: Any, collection_name: str, embedding_function: Callable, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata')[source]#
Wrapper around Qdrant vector database.
To use you should have the qdrant-client package installed.
Example
from langchain import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
CONTENT_KEY = 'page_content'#
METADATA_KEY = 'metadata'#
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
Returns
List of ids from adding the texts into the vectorstore.
classmethod from_documents(documents: List[langchain.docstore.document.Document], embedding: langchain.embeddings.base.Embeddings, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: Optional[bool] = None, api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[float] = None, host: Optional[str] = None, collection_name: Optional[str] = None, distance_func: str = 'Cosine', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', **kwargs: Any) → langchain.vectorstores.qdrant.Qdrant[source]#